Demand for automatic surveillance system has increased drastically at public places for variety of reasons. Most of the available surveillance systems are based on video information captured from surveillance cameras. However, it is difficult to track the abnormal events from video information because visual analysis has many practical limitations. The visual analysis has practical limitations such as deployment of cameras and external environmental lighting conditions. Video information based surveillance system fails due to abnormal weather conditions such as low lighting effects, fog and darkness etc.
Further, in order to overcome the shortcomings of visual analysis in surveillance system, audio based surveillance systems are developed. Audio based surveillance systems are developed to detect the events like pitch range, gunshot, glass breaking, fighting, dog barking, vocal and non-vocal events etc. However, the audio based surveillance systems fails in detecting the above said events in noisy conditions.
There are a variety of audio and video based surveillance systems proposed in the art using different features to detect any abnormal events or situations in public places. It was observed in the prior art that combination of different features did not result in improved classification. On the contrary, it is disclosed in the prior art that use of combination of features give reduction in performance of the surveillance systems.